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A clear connection between basic research and applied management is often missing or difficult to discern. We present a case study of integration of basic research with applied management for estimating abundance of gray wolves (Canis lupus) in Montana, USA. Estimating wolf abundance is a key component of wolf management but is costly and time intensive as wolf populations continue to grow. We developed a multimodel approach using an occupancy model, mechanistic territory model, and empirical group size model to improve abundance estimates while reducing monitoring effort. Whereas field‐based wolf counts generally rely on costly, difficult‐to‐collect monitoring data, especially for larger areas or population sizes, our approach efficiently uses readily available wolf observation data and introduces models focused on biological mechanisms underlying territorial and social behavior. In a three‐part process, the occupancy model first estimates the extent of wolf distribution in Montana, based on environmental covariates and wolf observations. The spatially explicit mechanistic territory model predicts territory sizes using simple behavioral rules and data on prey resources, terrain ruggedness, and human density. Together, these models predict the number of packs. An empirical pack size model based on 14 years of data demonstrates that pack sizes are positively related to local densities of packs, and negatively related to terrain ruggedness, local mortalities, and intensity of harvest management. Total abundance estimates for given areas are derived by combining estimated numbers of packs and pack sizes. We estimated the Montana wolf population to be smallest in the first year of our study, with 91 packs and 654 wolves in 2007, followed by a population peak in 2011 with 1252 wolves. The population declined ~6% thereafter, coincident with implementation of legal harvest in Montana. Recent numbers have largely stabilized at an average of 191 packs and 1141 wolves from 2016 to 2020. This new approach accounts for biologically based, spatially explicit predictions of behavior to provide more accurate estimates of carnivore abundance at finer spatial scales. By integrating basic and applied research, our approach can therefore better inform decision‐making and meet management needs. [ABSTRACT FROM AUTHOR]

Climate change is expected to disproportionately affect species occupying ecosystems with relatively hard boundaries, such as alpine ecosystems. Wildlife managers must identify actions to conserve and manage alpine species into the future, while considering other issues and uncertainties. Climate change and respiratory pathogens associated with widespread pneumonia epidemics in bighorn sheep (Ovis canadensis) may negatively affect mountain goat (Oreamnos americanus) populations. Mountain goat demographic and population data are challenging to collect and sparsely available, making population management decisions difficult. We developed predictive models incorporating these uncertainties and analyzed results within a structured decision making framework to make management recommendations and identify priority information needs in Montana, USA. We built resource selection models to forecast occupied mountain goat habitat and account for uncertainty in effects of climate change, and a Leslie matrix projection model to predict population trends while accounting for uncertainty in population demographics and dynamics. We predicted disease risks while accounting for uncertainty about presence of pneumonia pathogens and risk tolerance for mixing populations during translocations. Our analysis predicted that new introductions would produce more area occupied by mountain goats at mid‐century, regardless of the effects of climate change. Population augmentations, carnivore management, and harvest management may improve population trends, although this was associated with considerable uncertainty. Tolerance for risk of disease transmission affected optimal management choices because translocations are expected to increase disease risks for mountain goats and sympatric bighorn sheep. Expected value of information analyses revealed that reducing uncertainty related to population dynamics would affect the optimal choice among management strategies to improve mountain goat trends. Reducing uncertainty related to the presence of pneumonia‐associated pathogens and consequences of mixing microbial communities should reduce disease risks if translocations are included in future management strategies. We recommend managers determine tolerance for disease risks associated with translocations that they and constituents are willing to accept. From this, an adaptive management program can be constructed wherein a portfolio of management actions are chosen based on risk tolerance in each population range, combined with the amount that uncertainty is reduced when paired with monitoring, to ultimately improve achievement of fundamental objectives. [ABSTRACT FROM AUTHOR]

Group living is found in only 10–15% of carnivorans and can shape demographic processes. Sociality is associated with benefits including increased ability to acquire resources, decreased risk of mortality, and increased reproductive success. We hypothesized that carnivore group size is influenced by conditions related to competition, prey, and mortality risk, which should affect benefits and costs of sociality and resulting demographic processes. We evaluated our hypotheses with gray wolves (Canis lupus) using a 14‐year dataset from a large, heavily managed population in the northern Rocky Mountains, USA. Annual mean group size ranged 4.86–7.03 and averaged 5.92 overall. Most groups were relatively small, with 80% containing ≤8 members. Groups were larger in areas with higher densities of conspecific groups, and smaller where prey availability was low. Group sizes remained largely stable while the population was unharvested or under low‐intensity harvest but declined under high‐intensity harvest. Results support the hypothesis that as habitat becomes saturated, inclusive fitness may become increasingly important such that subordinates delay dispersal. In addition to direct implications for birth and deaths, conditions related to prey and mortality risk may also influence dispersal decisions. Our work also provided a model to predict group size of wolves in our system, directly fulfilling a management need. [ABSTRACT FROM AUTHOR]

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern‐oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent‐based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM‐ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM‐ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large. [ABSTRACT FROM AUTHOR]

ABSTRACT: Rigorous science that produces reliable knowledge is critical to wildlife management because it increases accurate understanding of the natural world and informs management decisions effectively. Application of a rigorous scientific method based on hypothesis testing minimizes unreliable knowledge produced by research. To evaluate the prevalence of scientific rigor in wildlife research, we examined 24 issues of the Journal of Wildlife Management from August 2013 through July 2016. We found 43.9% of studies did not state or imply a priori hypotheses, which are necessary to produce reliable knowledge. We posit that this is due, at least in part, to a lack of common understanding of what rigorous science entails, how it produces more reliable knowledge than other forms of interpreting observations, and how research should be designed to maximize inferential strength and usefulness of application. Current primary literature does not provide succinct explanations of the logic behind a rigorous scientific method or readily applicable guidance for employing it, particularly in wildlife biology; we therefore synthesized an overview of the history, philosophy, and logic that define scientific rigor for biological studies. A rigorous scientific method includes 1) generating a research question from theory and prior observations, 2) developing hypotheses (i.e., plausible biological answers to the question), 3) formulating predictions (i.e., facts that must be true if the hypothesis is true), 4) designing and implementing research to collect data potentially consistent with predictions, 5) evaluating whether predictions are consistent with collected data, and 6) drawing inferences based on the evaluation. Explicitly testing a priori hypotheses reduces overall uncertainty by reducing the number of plausible biological explanations to only those that are logically well supported. Such research also draws inferences that are robust to idiosyncratic observations and unavoidable human biases. Offering only post hoc interpretations of statistical patterns (i.e., a posteriori hypotheses) adds to uncertainty because it increases the number of plausible biological explanations without determining which have the greatest support. Further, post hoc interpretations are strongly subject to human biases. Testing hypotheses maximizes the credibility of research findings, makes the strongest contributions to theory and management, and improves reproducibility of research. Management decisions based on rigorous research are most likely to result in effective conservation of wildlife resources. © 2018 The Wildlife Society. [ABSTRACT FROM AUTHOR]

ABSTRACT: The relative roles of science and human values can be difficult to distinguish when informal processes are used to make complex and contentious decisions in wildlife management. Structured Decision Making (SDM) offers a formal process for making such decisions, where scientific results and concepts can be disentangled from the values of differing stakeholders. We used SDM to formally integrate science and human values for a citizen working group of ungulate hunting advocates, lion hunting advocates, and outfitters convened to address the contentious allocation of harvest quotas for mountain lions (Puma concolor) in west‐central Montana, USA, during 2014. A science team consisting of mountain lion biologists and population ecologists convened to support the working group. The science team used integrated population models that incorporated 4 estimates of mountain lion density to estimate population trajectories for 5 alternative harvest quotas developed by the working group. Results of the modeling predicted that effects of each harvest quota were consistent across the 4 density estimates; harvest quotas affected predicted population trajectories for 5 years after implementation but differences were not strong. Based on these results, the focus of the working group changed to differences in values among stakeholders that were the true impediment to allocating harvest quotas. By distinguishing roles of science and human values in this process, the working group was able to collaboratively recommend a compromise solution. This solution differed little from the status quo that had been the focus of debate, but the SDM process produced understanding and buy‐in among stakeholders involved, reducing disagreements, misunderstanding, and unproductive arguments founded on informal application of scientific data and concepts. Whereas investments involved in conducting SDM may be unnecessary for many decisions in wildlife management, the investment may be beneficial for complex, contentious, and multiobjective decisions that integrate science and human values. © 2018 The Wildlife Society. [ABSTRACT FROM AUTHOR]

ABSTRACT Good decision-making is essential to conserving wildlife populations. Although there may be multiple ways to address a problem, perfect solutions rarely exist. Managers are therefore tasked with identifying decisions that will best achieve desired outcomes. Structured decision making (SDM) is a method of decision analysis used to identify the most effective, efficient, and realistic decisions while accounting for values and priorities of the decision maker. The stepwise process includes identifying the management problem, defining objectives for solving the problem, developing alternative approaches to achieve the objectives, and formally evaluating which alternative is most likely to accomplish the objectives. The SDM process can be more effective than informal decision-making because it provides a transparent way to quantitatively evaluate decisions for addressing multiple management objectives while incorporating science, uncertainty, and risk tolerance. To illustrate the application of this process to a management need, we present an SDM-based decision tool developed to identify optimal decisions for proactively managing risk of pneumonia epizootics in bighorn sheep ( Ovis canadensis) in Montana. Pneumonia epizootics are a major challenge for managers due to long-term impacts to herds, epistemic uncertainty in timing and location of future epizootics, and consequent difficulty knowing how or when to manage risk. The decision tool facilitates analysis of alternative decisions for how to manage herds based on predictions from a risk model, herd-specific objectives, and predicted costs and benefits of each alternative. Decision analyses for 2 example herds revealed that meeting management objectives necessitates specific approaches unique to each herd. The analyses showed how and under what circumstances the alternatives are optimal compared to other approaches and current management. Managers can be confident that these decisions are effective, efficient, and realistic because they explicitly account for important considerations managers implicitly weigh when making decisions, including competing management objectives, uncertainty in potential outcomes, and risk tolerance. © 2016 The Wildlife Society. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses articles within the issue on topics including improvement in wildlife management with increased scientific rigor and applicability of a priori hypotheses.

ABSTRACT Pneumonia epizootics are a major challenge for management of bighorn sheep ( Ovis canadensis) affecting persistence of herds, satisfaction of stakeholders, and allocations of resources by management agencies. Risk factors associated with the disease are poorly understood, making pneumonia epizootics hard to predict; such epizootics are thus managed reactively rather than proactively. We developed a model for herds in Montana that identifies risk factors and addresses biological questions about risk. Using Bayesian logistic regression with repeated measures, we found that private land, weed control using domestic sheep or goats, pneumonia history, and herd density were positively associated with risk of pneumonia epizootics in 43 herds that experienced 22 epizootics out of 637 herd-years from 1979-2013. We defined an area of high risk for pathogen exposure as the area of each herd distribution plus a 14.5-km buffer from that boundary. Within this area, the odds of a pneumonia epizootic increased by >1.5 times per additional unit of private land (unit is the standardized % of private land where global [ABSTRACT FROM AUTHOR]